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Predictive head movement tracking using a Kalman filter.

A Kiruluta1, M Eizenman, S Pasupathy

  • 1Inst. of Biomed. Eng., Toronto, Ont.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1997
PubMed
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This study enhances head tracking for hands-free control using Kalman filters. The adaptive Kalman filter accurately predicts head positions, improving target acquisition in control applications.

Area of Science:

  • Robotics and Human-Computer Interaction
  • Control Systems Engineering
  • Biomedical Engineering

Background:

  • Head movements offer a hands-free method for controlling devices and tracking targets.
  • Existing methods may lack accuracy across diverse head dynamic characteristics.

Purpose of the Study:

  • To apply a Kalman filter for precise head position tracking in control applications.
  • To develop an adaptive approach for handling varied head dynamics.
  • To compare Kalman filter performance against polynomial predictors.

Main Methods:

  • Utilized a Kalman filter for generating head position prediction estimates.
  • Implemented a kinematics approach assuming piecewise constant acceleration.
  • Developed an adaptive Kalman filter with input estimation for dynamic characteristics.

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Main Results:

  • The Kalman filter achieved root-mean-square errors under 2 degrees for accelerations below 3000 degrees/s.
  • The adaptive approach improved tracking for a wide range of head dynamics.
  • Kalman filter performance surpassed that of a simple polynomial predictor.

Conclusions:

  • Kalman filtering provides accurate head position tracking for control applications.
  • Adaptive Kalman filtering effectively addresses varying head movement dynamics.
  • This method enhances hands-free control system performance and target acquisition.